This dissertation stresses the need and value of adding an Artificial Intelligence level between manufacturing units and First Principle Models that supply processing set points. The value is proven by evaluating the effect of designing and implementing a supervisory expert system for hot rolling process optimisation at Columbus Stainless Ltd. Pty. The hot rolling mill’s mainly fuzzy logic artificial intelligence “Expert System” functions as an extra diagnostic and control system that manages the performance of the processing set point models. The Expert System was developed in order to effectively imitate what the human experts used to do – which was to virtually continuously optimise the rolling process by making database data changes. The human experts had to make these process adjustments in order to compensate for the unavoidable imperfections and shortcomings of First Principle Models, which are unable to perfectly model nature. Even though the Expert System addresses a vast amount of hot rolling product quality and throughput control aspects, it still sufficiently automates human expert control to successfully manage production performance in all areas of control. This document was written in a format that would comply with Masters Degree Dissertation standards, while providing a document that can easily be used by Columbus Stainless personnel as a reference book of the philosophies, strategies and design of the Expert System.